"Team-building\nincentivized spokesperson",
"Global assessment,\nbefore leader",
"Global assessment,\nmale leader",
"Global assessment,\nfemale leader",
"Team-building influence,\nbefore leader",
"Team-building influence,\nmale leader",
"Team-building influence,\nfemale leader",
"Global influence,\nMonth 1",
"Global influence,\nMonth 2",
"Global influence,\nMonth 3",
"Global influence,\nMonth 4"), levels =
c("Team-building influence",
"Global assessment influence",
"Team-building\nincentivized spokesperson",
"Global assessment,\nbefore leader",
"Global assessment,\nmale leader",
"Global assessment,\nfemale leader",
"Team-building influence,\nbefore leader",
"Team-building influence,\nmale leader",
"Team-building influence,\nfemale leader",
"Global influence,\nMonth 1",
"Global influence,\nMonth 2",
"Global influence,\nMonth 3",
"Global influence,\nMonth 4"))
# means -- taken from graphs of figure 1 and related
# survey measures are from "surveyminfl_bymonth_forgraphing.csv"
# Data points from Figure 1:
task_influence <- read.csv("figure_1/intermediate_data/combined_groupgenderminfl_ratio_ravg_forgraphing.csv")
load(file = "../simulations/simulations_output_2.Rda")
# predict discrimination rate from observed X in inverted function
getX <- function(y, model) {
b <- coef(model)[1]
c <- coef(model)[2]
d <- coef(model)[3]
x <- -(sqrt(-4*b*d+ c^2 + 4*d*y) + c)/(2*d)
return(x)
}
outcomes <- factor(c("Team-building influence",
"Global assessment influence",
"Team-building\nincentivized spokesperson",
"Global assessment,\nbefore leader",
"Global assessment,\nmale leader",
"Global assessment,\nfemale leader",
"Team-building influence,\nbefore leader",
"Team-building influence,\nmale leader",
"Team-building influence,\nfemale leader",
"Global influence,\nMonth 1",
"Global influence,\nMonth 2",
"Global influence,\nMonth 3",
"Global influence,\nMonth 4"), levels =
c("Team-building influence",
"Global assessment influence",
"Team-building\nincentivized spokesperson",
"Global assessment,\nbefore leader",
"Global assessment,\nmale leader",
"Global assessment,\nfemale leader",
"Team-building influence,\nbefore leader",
"Team-building influence,\nmale leader",
"Team-building influence,\nfemale leader",
"Global influence,\nMonth 1",
"Global influence,\nMonth 2",
"Global influence,\nMonth 3",
"Global influence,\nMonth 4"))
# means -- taken from graphs of figure 1 and related
# survey measures are from "surveyminfl_bymonth_forgraphing.csv"
# Data points from Figure 1:
task_influence <- read.csv("../figure_1/intermediate_data/combined_groupgenderminfl_ratio_ravg_forgraphing.csv")
global_influence <- read.csv("../figure_1/intermediate_data/combined_groupfemvotesminfl_avgpre_ratio_forgraphing.csv")
spokesperson <- read.csv("../figure_1/intermediate_data/combined_genderspokes_ratio_ravg_forgraphing.csv")
# Data points from Figure 3:
minfl_ratio_r1 <- read.csv("../figure_1/intermediate_data/study2_groupgenderminfl_ratio_r1_forgraphing.csv")
minfl_ratio_r2_fem <- read.csv("../figure_3/intermediate_data/study2_newfig_groupgenderminfl_ratio_r2_fem_forgraphing.csv")
minfl_ratio_r2_mal <- read.csv("../figure_3/intermediate_data/study2_newfig_groupgenderminfl_ratio_r2_male_forgraphing.csv")
# Data points from Figure 4:
surveyminfl_bymonth <- read.csv("../figure_4/intermediate_data/surveyminfl_bymonth_forgraphing.csv")
# Data points from global assesment data:
femvotesminfl_pre <- read.csv("../figure_6/intermediate_data/study2_newfig_groupfemvotesminfl_avgpre_ratio_forgraphing.csv")
femvotesminfl_fem <- read.csv("../figure_6/intermediate_data/study2_newfig_groupfemvotesminfl_avgpost_ratio_fem_forgraphing.csv")
femvotesminfl_male <- read.csv("../figure_6/intermediate_data/study2_newfig_groupfemvotesminfl_avgpost_ratio_male_forgraphing.csv")
femMajValues  <- c(task_influence$v2[1], global_influence$v2[1], spokesperson$v2[1], # From Figure 1
femvotesminfl_pre$v2[1], femvotesminfl_male$v2[1], femvotesminfl_fem$v2[1], # From Global Assesment data.
minfl_ratio_r1$v2[1], # From Figure 1
minfl_ratio_r2_mal$v2[1], minfl_ratio_r2_fem$v6[1], # From Figure 3
surveyminfl_bymonth$estimate[2], surveyminfl_bymonth$estimate[4], # From Figure 4
surveyminfl_bymonth$estimate[6], surveyminfl_bymonth$estimate[8]) # From Figure 4
maleMajValues <- c(task_influence$v2[2], global_influence$v2[2], spokesperson$v2[2], # From Figure 1
femvotesminfl_pre$v2[2], femvotesminfl_male$v2[1], femvotesminfl_fem$v2[2],  # From Global Assesment data.
minfl_ratio_r1$v2[2], # From Figure 1
minfl_ratio_r2_mal$v2[2], minfl_ratio_r2_fem$v6[2], # From Figure 3
surveyminfl_bymonth$estimate[1], surveyminfl_bymonth$estimate[3],
surveyminfl_bymonth$estimate[5], surveyminfl_bymonth$estimate[7]) # From Figure 4
#femMajValues  <- c(.793, .856, 0.772, 0.856, 0.7435, 0.9615, .73, .641, .854, 0.824, 0.891, 0.856, 0.896)
#maleMajValues <- c(.447, .606, 0.499, 0.6965, 0.627, 0.899, .438, .419, .714, 0.376, 0.673, 0.731, 0.91)
femMajDiscrim <- getX(femMajValues, femaleMajorityTrend)
maleMajDiscrim <- getX(maleMajValues, maleMajorityTrend)
pointsFrame <- as.data.frame(cbind(femMajDiscrim, femMajValues, maleMajDiscrim, maleMajValues))
pointsFrame <- cbind(pointsFrame, outcomes)
loopOut.df.aggregated$predictionFemMaj <- getX(loopOut.df.aggregated$FvoteRatioBest3F, femaleMajorityTrend)
loopOut.df.aggregated$predictionMaleMaj <- getX(loopOut.df.aggregated$FvoteRatioBest1F, maleMajorityTrend)
# set graph option parameters
colorFemaleMaj<-"#E69F00"
colorMaleMaj<- "#56B4E9"
gphLinewidth <- 1.5
gphPointSize <- 5
gphAxisLabFontSize <- 28
# outcomes
subpointsFrame<- pointsFrame[1:3,]
ggplot(data = loopOut.df.aggregated) +
geom_line(aes(x = predictionMaleMaj, y = FvoteRatioBest1F, color=colorMaleMaj ), linewidth = gphLinewidth) +
geom_line(aes(x = predictionFemMaj, y = FvoteRatioBest3F, color=colorFemaleMaj), linewidth = gphLinewidth) +
geom_point(data = subpointsFrame, aes(x = femMajDiscrim, y = femMajValues, shape = outcomes), color = colorFemaleMaj, size = gphPointSize) +
geom_point(data = subpointsFrame, aes(x = maleMajDiscrim, y = maleMajValues, shape = outcomes), color = colorMaleMaj, size = gphPointSize) +
ylab("votes per woman") + xlab("Performance penalty for women (SD)") +
geom_hline(yintercept = 1, linetype = 'dashed', color="black") +
scale_color_manual(values=c(colorMaleMaj, colorFemaleMaj),
labels = c("Male-majority vote series",
"Female-majority vote series")) +
theme(legend.title=element_blank(), legend.position = c(.9, .85)) +
theme(text=element_text(size=12),
axis.title=element_text(size=gphAxisLabFontSize),
axis.text=element_text(size=gphAxisLabFontSize-6),
plot.title = element_text(hjust=.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title.x=element_blank(),
axis.line = element_line(colour="black"))+
guides(shape = guide_legend(override.aes = list(color = "grey"))) +
scale_y_continuous(breaks=seq(0,1.0,.2), expand = c(0,0), limits = c(0, 1.2)) +   scale_x_continuous(breaks=seq(-.10,1.10,.10), expand = c(0,0), limits = c(-.2,1.2))
ggsave(file = 'figure_6/figure_6a.pdf', width = 12, height = 7, units = "in")
load(file = "../simulations/simulations_output_2.Rda")
# predict discrimination rate from observed X in inverted function
getX <- function(y, model) {
b <- coef(model)[1]
c <- coef(model)[2]
d <- coef(model)[3]
x <- -(sqrt(-4*b*d+ c^2 + 4*d*y) + c)/(2*d)
return(x)
}
outcomes <- factor(c("Team-building influence",
"Global assessment influence",
"Team-building\nincentivized spokesperson",
"Global assessment,\nbefore leader",
"Global assessment,\nmale leader",
"Global assessment,\nfemale leader",
"Team-building influence,\nbefore leader",
"Team-building influence,\nmale leader",
"Team-building influence,\nfemale leader",
"Global influence,\nMonth 1",
"Global influence,\nMonth 2",
"Global influence,\nMonth 3",
"Global influence,\nMonth 4"), levels =
c("Team-building influence",
"Global assessment influence",
"Team-building\nincentivized spokesperson",
"Global assessment,\nbefore leader",
"Global assessment,\nmale leader",
"Global assessment,\nfemale leader",
"Team-building influence,\nbefore leader",
"Team-building influence,\nmale leader",
"Team-building influence,\nfemale leader",
"Global influence,\nMonth 1",
"Global influence,\nMonth 2",
"Global influence,\nMonth 3",
"Global influence,\nMonth 4"))
# means -- taken from graphs of figure 1 and related
# survey measures are from "surveyminfl_bymonth_forgraphing.csv"
# Data points from Figure 1:
task_influence <- read.csv("../figure_1/intermediate_data/combined_groupgenderminfl_ratio_ravg_forgraphing.csv")
global_influence <- read.csv("../figure_1/intermediate_data/combined_groupfemvotesminfl_avgpre_ratio_forgraphing.csv")
spokesperson <- read.csv("../figure_1/intermediate_data/combined_genderspokes_ratio_ravg_forgraphing.csv")
# Data points from Figure 3:
minfl_ratio_r1 <- read.csv("../figure_1/intermediate_data/study2_groupgenderminfl_ratio_r1_forgraphing.csv")
minfl_ratio_r2_fem <- read.csv("../figure_3/intermediate_data/study2_newfig_groupgenderminfl_ratio_r2_fem_forgraphing.csv")
minfl_ratio_r2_mal <- read.csv("../figure_3/intermediate_data/study2_newfig_groupgenderminfl_ratio_r2_male_forgraphing.csv")
# Data points from Figure 4:
surveyminfl_bymonth <- read.csv("../figure_4/intermediate_data/surveyminfl_bymonth_forgraphing.csv")
# Data points from global assesment data:
femvotesminfl_pre <- read.csv("../figure_6/intermediate_data/study2_newfig_groupfemvotesminfl_avgpre_ratio_forgraphing.csv")
femvotesminfl_fem <- read.csv("../figure_6/intermediate_data/study2_newfig_groupfemvotesminfl_avgpost_ratio_fem_forgraphing.csv")
femvotesminfl_male <- read.csv("../figure_6/intermediate_data/study2_newfig_groupfemvotesminfl_avgpost_ratio_male_forgraphing.csv")
femMajValues  <- c(task_influence$v2[1], global_influence$v2[1], spokesperson$v2[1], # From Figure 1
femvotesminfl_pre$v2[1], femvotesminfl_male$v2[1], femvotesminfl_fem$v2[1], # From Global Assesment data.
minfl_ratio_r1$v2[1], # From Figure 1
minfl_ratio_r2_mal$v2[1], minfl_ratio_r2_fem$v6[1], # From Figure 3
surveyminfl_bymonth$estimate[2], surveyminfl_bymonth$estimate[4], # From Figure 4
surveyminfl_bymonth$estimate[6], surveyminfl_bymonth$estimate[8]) # From Figure 4
maleMajValues <- c(task_influence$v2[2], global_influence$v2[2], spokesperson$v2[2], # From Figure 1
femvotesminfl_pre$v2[2], femvotesminfl_male$v2[1], femvotesminfl_fem$v2[2],  # From Global Assesment data.
minfl_ratio_r1$v2[2], # From Figure 1
minfl_ratio_r2_mal$v2[2], minfl_ratio_r2_fem$v6[2], # From Figure 3
surveyminfl_bymonth$estimate[1], surveyminfl_bymonth$estimate[3],
surveyminfl_bymonth$estimate[5], surveyminfl_bymonth$estimate[7]) # From Figure 4
#femMajValues  <- c(.793, .856, 0.772, 0.856, 0.7435, 0.9615, .73, .641, .854, 0.824, 0.891, 0.856, 0.896)
#maleMajValues <- c(.447, .606, 0.499, 0.6965, 0.627, 0.899, .438, .419, .714, 0.376, 0.673, 0.731, 0.91)
femMajDiscrim <- getX(femMajValues, femaleMajorityTrend)
maleMajDiscrim <- getX(maleMajValues, maleMajorityTrend)
pointsFrame <- as.data.frame(cbind(femMajDiscrim, femMajValues, maleMajDiscrim, maleMajValues))
pointsFrame <- cbind(pointsFrame, outcomes)
loopOut.df.aggregated$predictionFemMaj <- getX(loopOut.df.aggregated$FvoteRatioBest3F, femaleMajorityTrend)
loopOut.df.aggregated$predictionMaleMaj <- getX(loopOut.df.aggregated$FvoteRatioBest1F, maleMajorityTrend)
# set graph option parameters
colorFemaleMaj<-"#E69F00"
colorMaleMaj<- "#56B4E9"
gphLinewidth <- 1.5
gphPointSize <- 5
gphAxisLabFontSize <- 28
# outcomes
subpointsFrame<- pointsFrame[1:3,]
ggplot(data = loopOut.df.aggregated) +
geom_line(aes(x = predictionMaleMaj, y = FvoteRatioBest1F, color=colorMaleMaj ), linewidth = gphLinewidth) +
geom_line(aes(x = predictionFemMaj, y = FvoteRatioBest3F, color=colorFemaleMaj), linewidth = gphLinewidth) +
geom_point(data = subpointsFrame, aes(x = femMajDiscrim, y = femMajValues, shape = outcomes), color = colorFemaleMaj, size = gphPointSize) +
geom_point(data = subpointsFrame, aes(x = maleMajDiscrim, y = maleMajValues, shape = outcomes), color = colorMaleMaj, size = gphPointSize) +
ylab("votes per woman") + xlab("Performance penalty for women (SD)") +
geom_hline(yintercept = 1, linetype = 'dashed', color="black") +
scale_color_manual(values=c(colorMaleMaj, colorFemaleMaj),
labels = c("Male-majority vote series",
"Female-majority vote series")) +
theme(legend.title=element_blank(), legend.position = c(.9, .85)) +
theme(text=element_text(size=12),
axis.title=element_text(size=gphAxisLabFontSize),
axis.text=element_text(size=gphAxisLabFontSize-6),
plot.title = element_text(hjust=.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title.x=element_blank(),
axis.line = element_line(colour="black"))+
guides(shape = guide_legend(override.aes = list(color = "grey"))) +
scale_y_continuous(breaks=seq(0,1.0,.2), expand = c(0,0), limits = c(0, 1.2)) +   scale_x_continuous(breaks=seq(-.10,1.10,.10), expand = c(0,0), limits = c(-.2,1.2))
ggsave(file = 'figure_6/figure_6a.pdf', width = 12, height = 7, units = "in")
load(file = "../simulations/simulations_output_2.Rda")
# predict discrimination rate from observed X in inverted function
getX <- function(y, model) {
b <- coef(model)[1]
c <- coef(model)[2]
d <- coef(model)[3]
x <- -(sqrt(-4*b*d+ c^2 + 4*d*y) + c)/(2*d)
return(x)
}
outcomes <- factor(c("Team-building influence",
"Global assessment influence",
"Team-building\nincentivized spokesperson",
"Global assessment,\nbefore leader",
"Global assessment,\nmale leader",
"Global assessment,\nfemale leader",
"Team-building influence,\nbefore leader",
"Team-building influence,\nmale leader",
"Team-building influence,\nfemale leader",
"Global influence,\nMonth 1",
"Global influence,\nMonth 2",
"Global influence,\nMonth 3",
"Global influence,\nMonth 4"), levels =
c("Team-building influence",
"Global assessment influence",
"Team-building\nincentivized spokesperson",
"Global assessment,\nbefore leader",
"Global assessment,\nmale leader",
"Global assessment,\nfemale leader",
"Team-building influence,\nbefore leader",
"Team-building influence,\nmale leader",
"Team-building influence,\nfemale leader",
"Global influence,\nMonth 1",
"Global influence,\nMonth 2",
"Global influence,\nMonth 3",
"Global influence,\nMonth 4"))
# means -- taken from graphs of figure 1 and related
# survey measures are from "surveyminfl_bymonth_forgraphing.csv"
# Data points from Figure 1:
task_influence <- read.csv("../figure_1/intermediate_data/combined_groupgenderminfl_ratio_ravg_forgraphing.csv")
global_influence <- read.csv("../figure_1/intermediate_data/combined_groupfemvotesminfl_avgpre_ratio_forgraphing.csv")
spokesperson <- read.csv("../figure_1/intermediate_data/combined_genderspokes_ratio_ravg_forgraphing.csv")
# Data points from Figure 3:
minfl_ratio_r1 <- read.csv("../figure_1/intermediate_data/study2_groupgenderminfl_ratio_r1_forgraphing.csv")
minfl_ratio_r2_fem <- read.csv("../figure_3/intermediate_data/study2_newfig_groupgenderminfl_ratio_r2_fem_forgraphing.csv")
minfl_ratio_r2_mal <- read.csv("../figure_3/intermediate_data/study2_newfig_groupgenderminfl_ratio_r2_male_forgraphing.csv")
# Data points from Figure 4:
surveyminfl_bymonth <- read.csv("../figure_4/intermediate_data/surveyminfl_bymonth_forgraphing.csv")
# Data points from global assesment data:
femvotesminfl_pre <- read.csv("../figure_6/intermediate_data/study2_newfig_groupfemvotesminfl_avgpre_ratio_forgraphing.csv")
femvotesminfl_fem <- read.csv("../figure_6/intermediate_data/study2_newfig_groupfemvotesminfl_avgpost_ratio_fem_forgraphing.csv")
femvotesminfl_male <- read.csv("../figure_6/intermediate_data/study2_newfig_groupfemvotesminfl_avgpost_ratio_male_forgraphing.csv")
femMajValues  <- c(task_influence$v2[1], global_influence$v2[1], spokesperson$v2[1], # From Figure 1
femvotesminfl_pre$v2[1], femvotesminfl_male$v2[1], femvotesminfl_fem$v2[1], # From Global Assesment data.
minfl_ratio_r1$v2[1], # From Figure 1
minfl_ratio_r2_mal$v2[1], minfl_ratio_r2_fem$v6[1], # From Figure 3
surveyminfl_bymonth$estimate[2], surveyminfl_bymonth$estimate[4], # From Figure 4
surveyminfl_bymonth$estimate[6], surveyminfl_bymonth$estimate[8]) # From Figure 4
maleMajValues <- c(task_influence$v2[2], global_influence$v2[2], spokesperson$v2[2], # From Figure 1
femvotesminfl_pre$v2[2], femvotesminfl_male$v2[1], femvotesminfl_fem$v2[2],  # From Global Assesment data.
minfl_ratio_r1$v2[2], # From Figure 1
minfl_ratio_r2_mal$v2[2], minfl_ratio_r2_fem$v6[2], # From Figure 3
surveyminfl_bymonth$estimate[1], surveyminfl_bymonth$estimate[3],
surveyminfl_bymonth$estimate[5], surveyminfl_bymonth$estimate[7]) # From Figure 4
#femMajValues  <- c(.793, .856, 0.772, 0.856, 0.7435, 0.9615, .73, .641, .854, 0.824, 0.891, 0.856, 0.896)
#maleMajValues <- c(.447, .606, 0.499, 0.6965, 0.627, 0.899, .438, .419, .714, 0.376, 0.673, 0.731, 0.91)
femMajDiscrim <- getX(femMajValues, femaleMajorityTrend)
maleMajDiscrim <- getX(maleMajValues, maleMajorityTrend)
pointsFrame <- as.data.frame(cbind(femMajDiscrim, femMajValues, maleMajDiscrim, maleMajValues))
pointsFrame <- cbind(pointsFrame, outcomes)
loopOut.df.aggregated$predictionFemMaj <- getX(loopOut.df.aggregated$FvoteRatioBest3F, femaleMajorityTrend)
loopOut.df.aggregated$predictionMaleMaj <- getX(loopOut.df.aggregated$FvoteRatioBest1F, maleMajorityTrend)
# set graph option parameters
colorFemaleMaj<-"#E69F00"
colorMaleMaj<- "#56B4E9"
gphLinewidth <- 1.5
gphPointSize <- 5
gphAxisLabFontSize <- 28
# outcomes
subpointsFrame<- pointsFrame[1:3,]
ggplot(data = loopOut.df.aggregated) +
geom_line(aes(x = predictionMaleMaj, y = FvoteRatioBest1F, color=colorMaleMaj ), linewidth = gphLinewidth) +
geom_line(aes(x = predictionFemMaj, y = FvoteRatioBest3F, color=colorFemaleMaj), linewidth = gphLinewidth) +
geom_point(data = subpointsFrame, aes(x = femMajDiscrim, y = femMajValues, shape = outcomes), color = colorFemaleMaj, size = gphPointSize) +
geom_point(data = subpointsFrame, aes(x = maleMajDiscrim, y = maleMajValues, shape = outcomes), color = colorMaleMaj, size = gphPointSize) +
ylab("votes per woman") + xlab("Performance penalty for women (SD)") +
geom_hline(yintercept = 1, linetype = 'dashed', color="black") +
scale_color_manual(values=c(colorMaleMaj, colorFemaleMaj),
labels = c("Male-majority vote series",
"Female-majority vote series")) +
theme(legend.title=element_blank(), legend.position = c(.9, .85)) +
theme(text=element_text(size=12),
axis.title=element_text(size=gphAxisLabFontSize),
axis.text=element_text(size=gphAxisLabFontSize-6),
plot.title = element_text(hjust=.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title.x=element_blank(),
axis.line = element_line(colour="black"))+
guides(shape = guide_legend(override.aes = list(color = "grey"))) +
scale_y_continuous(breaks=seq(0,1.0,.2), expand = c(0,0), limits = c(0, 1.2)) +   scale_x_continuous(breaks=seq(-.10,1.10,.10), expand = c(0,0), limits = c(-.2,1.2))
ggsave(file = 'figure_6/figure_6a.pdf', width = 12, height = 7, units = "in")
ggsave(file = 'figure_6d.pdf', width = 12, height = 7, units = "in")
load(file = "../simulations/simulations_output_2.Rda")
# predict discrimination rate from observed X in inverted function
getX <- function(y, model) {
b <- coef(model)[1]
c <- coef(model)[2]
d <- coef(model)[3]
x <- -(sqrt(-4*b*d+ c^2 + 4*d*y) + c)/(2*d)
return(x)
}
outcomes <- factor(c("Team-building influence",
"Global assessment influence",
"Team-building\nincentivized spokesperson",
"Global assessment,\nbefore leader",
"Global assessment,\nmale leader",
"Global assessment,\nfemale leader",
"Team-building influence,\nbefore leader",
"Team-building influence,\nmale leader",
"Team-building influence,\nfemale leader",
"Global influence,\nMonth 1",
"Global influence,\nMonth 2",
"Global influence,\nMonth 3",
"Global influence,\nMonth 4"), levels =
c("Team-building influence",
"Global assessment influence",
"Team-building\nincentivized spokesperson",
"Global assessment,\nbefore leader",
"Global assessment,\nmale leader",
"Global assessment,\nfemale leader",
"Team-building influence,\nbefore leader",
"Team-building influence,\nmale leader",
"Team-building influence,\nfemale leader",
"Global influence,\nMonth 1",
"Global influence,\nMonth 2",
"Global influence,\nMonth 3",
"Global influence,\nMonth 4"))
# means -- taken from graphs of figure 1 and related
# survey measures are from "surveyminfl_bymonth_forgraphing.csv"
# Data points from Figure 1:
task_influence <- read.csv("../figure_1/intermediate_data/combined_groupgenderminfl_ratio_ravg_forgraphing.csv")
global_influence <- read.csv("../figure_1/intermediate_data/combined_groupfemvotesminfl_avgpre_ratio_forgraphing.csv")
spokesperson <- read.csv("../figure_1/intermediate_data/combined_genderspokes_ratio_ravg_forgraphing.csv")
# Data points from Figure 3:
minfl_ratio_r1 <- read.csv("../figure_1/intermediate_data/study2_groupgenderminfl_ratio_r1_forgraphing.csv")
minfl_ratio_r2_fem <- read.csv("../figure_3/intermediate_data/study2_newfig_groupgenderminfl_ratio_r2_fem_forgraphing.csv")
minfl_ratio_r2_mal <- read.csv("../figure_3/intermediate_data/study2_newfig_groupgenderminfl_ratio_r2_male_forgraphing.csv")
# Data points from Figure 4:
surveyminfl_bymonth <- read.csv("../figure_4/intermediate_data/surveyminfl_bymonth_forgraphing.csv")
# Data points from global assesment data:
femvotesminfl_pre <- read.csv("../figure_6/intermediate_data/study2_newfig_groupfemvotesminfl_avgpre_ratio_forgraphing.csv")
femvotesminfl_fem <- read.csv("../figure_6/intermediate_data/study2_newfig_groupfemvotesminfl_avgpost_ratio_fem_forgraphing.csv")
femvotesminfl_male <- read.csv("../figure_6/intermediate_data/study2_newfig_groupfemvotesminfl_avgpost_ratio_male_forgraphing.csv")
femMajValues  <- c(task_influence$v2[1], global_influence$v2[1], spokesperson$v2[1], # From Figure 1
femvotesminfl_pre$v2[1], femvotesminfl_male$v2[1], femvotesminfl_fem$v2[1], # From Global Assesment data.
minfl_ratio_r1$v2[1], # From Figure 1
minfl_ratio_r2_mal$v2[1], minfl_ratio_r2_fem$v6[1], # From Figure 3
surveyminfl_bymonth$estimate[2], surveyminfl_bymonth$estimate[4], # From Figure 4
surveyminfl_bymonth$estimate[6], surveyminfl_bymonth$estimate[8]) # From Figure 4
maleMajValues <- c(task_influence$v2[2], global_influence$v2[2], spokesperson$v2[2], # From Figure 1
femvotesminfl_pre$v2[2], femvotesminfl_male$v2[1], femvotesminfl_fem$v2[2],  # From Global Assesment data.
minfl_ratio_r1$v2[2], # From Figure 1
minfl_ratio_r2_mal$v2[2], minfl_ratio_r2_fem$v6[2], # From Figure 3
surveyminfl_bymonth$estimate[1], surveyminfl_bymonth$estimate[3],
surveyminfl_bymonth$estimate[5], surveyminfl_bymonth$estimate[7]) # From Figure 4
#femMajValues  <- c(.793, .856, 0.772, 0.856, 0.7435, 0.9615, .73, .641, .854, 0.824, 0.891, 0.856, 0.896)
#maleMajValues <- c(.447, .606, 0.499, 0.6965, 0.627, 0.899, .438, .419, .714, 0.376, 0.673, 0.731, 0.91)
femMajDiscrim <- getX(femMajValues, femaleMajorityTrend)
maleMajDiscrim <- getX(maleMajValues, maleMajorityTrend)
pointsFrame <- as.data.frame(cbind(femMajDiscrim, femMajValues, maleMajDiscrim, maleMajValues))
pointsFrame <- cbind(pointsFrame, outcomes)
loopOut.df.aggregated$predictionFemMaj <- getX(loopOut.df.aggregated$FvoteRatioBest3F, femaleMajorityTrend)
loopOut.df.aggregated$predictionMaleMaj <- getX(loopOut.df.aggregated$FvoteRatioBest1F, maleMajorityTrend)
# set graph option parameters
colorFemaleMaj<-"#E69F00"
colorMaleMaj<- "#56B4E9"
gphLinewidth <- 1.5
gphPointSize <- 5
gphAxisLabFontSize <- 28
# outcomes
subpointsFrame<- pointsFrame[1:3,]
ggplot(data = loopOut.df.aggregated) +
geom_line(aes(x = predictionMaleMaj, y = FvoteRatioBest1F, color=colorMaleMaj ), linewidth = gphLinewidth) +
geom_line(aes(x = predictionFemMaj, y = FvoteRatioBest3F, color=colorFemaleMaj), linewidth = gphLinewidth) +
geom_point(data = subpointsFrame, aes(x = femMajDiscrim, y = femMajValues, shape = outcomes), color = colorFemaleMaj, size = gphPointSize) +
geom_point(data = subpointsFrame, aes(x = maleMajDiscrim, y = maleMajValues, shape = outcomes), color = colorMaleMaj, size = gphPointSize) +
ylab("votes per woman") + xlab("Performance penalty for women (SD)") +
geom_hline(yintercept = 1, linetype = 'dashed', color="black") +
scale_color_manual(values=c(colorMaleMaj, colorFemaleMaj),
labels = c("Male-majority vote series",
"Female-majority vote series")) +
theme(legend.title=element_blank(), legend.position = c(.9, .85)) +
theme(text=element_text(size=12),
axis.title=element_text(size=gphAxisLabFontSize),
axis.text=element_text(size=gphAxisLabFontSize-6),
plot.title = element_text(hjust=.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title.x=element_blank(),
axis.line = element_line(colour="black"))+
guides(shape = guide_legend(override.aes = list(color = "grey"))) +
scale_y_continuous(breaks=seq(0,1.0,.2), expand = c(0,0), limits = c(0, 1.2)) +   scale_x_continuous(breaks=seq(-.10,1.10,.10), expand = c(0,0), limits = c(-.2,1.2))
ggsave(file = 'figure_6a.pdf', width = 12, height = 7, units = "in")
# Leadership, global influence
subpointsFrame<- pointsFrame[4:6,]
ggplot(data = loopOut.df.aggregated) +
geom_line(aes(x = predictionMaleMaj, y = FvoteRatioBest1F, color=colorMaleMaj ), linewidth = gphLinewidth) +
geom_line(aes(x = predictionFemMaj, y = FvoteRatioBest3F, color=colorFemaleMaj), linewidth = gphLinewidth) +
geom_point(data = subpointsFrame, aes(x = femMajDiscrim, y = femMajValues, shape = outcomes), color = colorFemaleMaj, size = gphPointSize) +
geom_point(data = subpointsFrame, aes(x = maleMajDiscrim, y = maleMajValues, shape = outcomes), color = colorMaleMaj, size = gphPointSize) +
ylab("votes per woman") + xlab("Performance penalty for women (SD)") +
geom_hline(yintercept = 1, linetype = 'dashed', color="black") +
scale_color_manual(values=c(colorMaleMaj, colorFemaleMaj),
labels = c("Male-majority vote series",
"Female-majority vote series")) +
theme(legend.title=element_blank(), legend.position = c(.9, .85)) +
theme(text=element_text(size=12),
axis.title=element_text(size=gphAxisLabFontSize),
axis.text=element_text(size=gphAxisLabFontSize-6),
plot.title = element_text(hjust=.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour="black"))+
guides(shape = guide_legend(override.aes = list(color = "grey"))) +
scale_y_continuous(breaks=seq(0,1.0,.2), expand = c(0,0), limits = c(0, 1.2)) +   scale_x_continuous(breaks=seq(-.10,1.10,.10), expand = c(0,0), limits = c(-.2,1.20))
ggsave( file = 'figure_6c.pdf', width = 12, height = 7, units = "in")
# leadership task
subpointsFrame<- pointsFrame[7:9,]
ggplot(data = loopOut.df.aggregated) +
geom_line(aes(x = predictionMaleMaj, y = FvoteRatioBest1F, color=colorMaleMaj ), linewidth = gphLinewidth) +
geom_line(aes(x = predictionFemMaj, y = FvoteRatioBest3F, color=colorFemaleMaj), linewidth = gphLinewidth) +
geom_point(data = subpointsFrame, aes(x = femMajDiscrim, y = femMajValues, shape = outcomes), color = colorFemaleMaj, size = gphPointSize) +
geom_point(data = subpointsFrame, aes(x = maleMajDiscrim, y = maleMajValues, shape = outcomes), color = colorMaleMaj, size = gphPointSize) +
ylab("votes per woman") + xlab("Performance penalty for women (SD)") +
geom_hline(yintercept = 1, linetype = 'dashed', color="black") +
scale_color_manual(values=c(colorMaleMaj, colorFemaleMaj),
labels = c("Male-majority vote series",
"Female-majority vote series")) +
theme(legend.title=element_blank(), legend.position = c(.9, .85)) +
theme(text=element_text(size=12),
axis.title=element_text(size=gphAxisLabFontSize),
axis.text=element_text(size=gphAxisLabFontSize-10),
plot.title = element_text(hjust=.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
axis.line = element_line(colour="black"))+
guides(shape = guide_legend(override.aes = list(color = "grey"))) +
scale_y_continuous(breaks=seq(0,1.0,.2), expand = c(0,0), limits = c(0, 1.2)) +   scale_x_continuous(breaks=seq(-.10,1.10,.10), expand = c(0,0), limits = c(-.2,1.20))
ggsave(file = 'figure_6b.pdf', width = 12, height = 7, units = "in")
subpointsFrame<- pointsFrame[10:13,]
subpointsFrame$monthLabel <- seq(1,4,1)
ggplot(data = loopOut.df.aggregated) +
geom_line(aes(x = predictionMaleMaj, y = FvoteRatioBest1F, color=colorMaleMaj ), linewidth = gphLinewidth) +
geom_line(aes(x = predictionFemMaj, y = FvoteRatioBest3F, color=colorFemaleMaj), linewidth = gphLinewidth) +
geom_text(data = subpointsFrame, aes(x = femMajDiscrim, y = femMajValues), label = subpointsFrame$monthLabel, size = gphPointSize+1) +
geom_text(data = subpointsFrame, aes(x = maleMajDiscrim, y = maleMajValues), label = subpointsFrame$monthLabel, size = gphPointSize+1) +
ylab("votes per woman") + xlab("Performance penalty for women (SD)") +
geom_hline(yintercept = 1, linetype = 'dashed', color="black") +
scale_color_manual(values=c(colorMaleMaj, colorFemaleMaj),
labels = c("Male-majority vote series",
"Female-majority vote series")) +
theme(legend.title=element_blank(), legend.position = c(.9, .85)) +
theme(text=element_text(size=12),
axis.title=element_text(size=gphAxisLabFontSize),
axis.text=element_text(size=gphAxisLabFontSize-6),
plot.title = element_text(hjust=.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.title.y=element_blank(),
axis.line = element_line(colour="black"))+
guides(shape = guide_legend(override.aes = list(color = "grey"))) +
scale_y_continuous(breaks=seq(0,1.0,.2), expand = c(0,0), limits = c(0, 1.2)) +   scale_x_continuous(breaks=seq(-.10,1.10,.10), expand = c(0,0), limits = c(-.2,1.20))
ggsave(file = 'figure_6d.pdf', width = 12, height = 7, units = "in")
